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Release Summary - Mar 21, 2024 (24.06)

The following key features and improvements, along with bug fixes, have been released in Algonomy CXP products in the release version 24.06 during Mar 07, 2024 - Mar 21, 2024.

Native First Party Instrumentation

We are excited to announce new first-party versions of our personalization cloud scripts, p13n.js and client.js, designed for more seamless integration into your e-commerce website. With this upgrade, we also introduce a new dedicated data capture domain: Here's what you need to know:

What's New?

Who Should Upgrade?

  • Those using p13n.js to track shopper behavior.
  • Users of client.js for dynamic website features.
  • Clients who use direct API calls from the browsers of their shoppers for tracking or to provide product suggestions are advised to modify their setup to utilize the new data capture domain (

Jira: ENG-27719

Enterprise Dashboard

Enabling Restriction Rules for Advanced Merchandising Results

Recommendation Restriction and Boosting rules, along with System Filters, are now applicable to Advanced Merchandising results. This integration allows for the application of filters to exclude products based on specific attribute values from recommendations. For example, during promotional periods, products not on sale can be excluded from being recommended.

Furthermore, general system filters, which were previously applied automatically to recommendations, can now be applied to Advanced Merchandising rules as well. These system filters include:

  • Excluding products without a valid image or link URL.
  • Filtering products based on their ratings and reviews.
  • Applying request-specific filters, such as refinements.

Jira: ENG-27722, ENG-27409

Composite Outfit - Show only recommendable products for merchandiser when they review outfits

Merchandisers are now equipped with a feature that filters out non-recommendable products when reviewing and selecting outfits for specific styles. This enhancement ensures that during the outfit curation process, only products eligible for recommendation are displayed, streamlining the selection by excluding items that do not meet the criteria for outfit generation.

Once outfits are generated for a given style, the selection pool for seed products is confined to recommendable products, ensuring relevancy and efficiency. Furthermore, any product search conducted within the styles and outfits pages will only display recommendable products. This refinement of search results aligns directly with the requirements for outfit generation, simplifying the merchandiser's task of finding suitable items.

Mar 21 2024.png

Jira: ENG-27733

Advanced Merchandising - show rule name as title

The Advanced Merchandising interface now features improvements that enhance usability for digital merchandisers. Rule names are prominently displayed in the page header once entered, ensuring easy identification, especially useful during editing. For new rules, the interface defaults to "Advanced Merchandising" until a name is specified.

The introduction of the ability to delete recommendation groups simplifies rule management, allowing for a cleaner workspace by removing unneeded rules. Furthermore, rule names remain visible in the interface, even when sections are collapsed, ensuring users always know which rule they are interacting with.

Jira: ENG-27683


Partial Match Support in recsForPlacementsContext API

The recsForPlacementsContext API now supports partial matches, enhancing the Guided Selling recommendations by ensuring users see enough suggestions, even with multiple filters applied. This feature allows for displaying products that partially match the applied filters, ranked by the number of matching attributes, ensuring more relevant recommendations.

Highlights include:

  • Activation through site configuration.
  • An optional API parameter for enabling partial results, defaulting to false for full matches.
  • Products are ranked and returned based on the count of matched attributes, improving result relevance.
  • Recommendation boosting is applied before filtering, optimizing product ordering.

Jira: ENG-27411

Allow Restriction Rules to be Applied to Advanced Merchandising Results 

Recommendation Restriction and Boosting rules and System Filters can now be applied to Advanced Merchandising results.

It is now possible to use Recommendation Restriction and Boosting rules with Advanced Merchandising rules. This enables you to apply a filter to not recommend products with a certain attribute value. For example, you could not recommend products that are not on sale during a promotional period.

This option also enables the general system filters that are automatically applied to recommendations to also be applied to Advanced Merchandising rules. Examples of these system filters are:

* not recommending products without a valid image or link URL

* filtering based on ratings and reviews values

* filters that are passed in the request, such as refinements

Jira: ENG-27409

General Availability: 29-Mar-2024

Data Engineering

Scorecard Report ThoughtSpot Visualizations

Created Worksheets and Liveboards in Thoughtspot for visualization of the following Scorecard Reports in the production environment:

  • Catalog Exposure Report
  • Missing Category Report
  • Product Exposure Report
  • Product Status Report
  • User Session Report



Jira: ENG-27916

General Availability: 29-Mar-2024

Other Feature Enhancements

The following feature enhancements and upgrades have been made in the release version 24.06 during Mar 07, 2024 - Mar 21, 2024.

Jira #



General Availability




Potential Log4Shell (CVE-2021-44228)

Java logging library log4j (version 2), called Log4Shell, was discovered that resulted in Remote Code Execution (RCE) by logging a certain string. Given how ubiquitous this library is, the severity of the exploit (full server control), and how easy it is to exploit, the impact of this vulnerability was quite severe.

As part of internal penetration testing on RROC in QA environment this vulnerability was detected and resolved now.





Call RR.setupGlassViews() from rrserver

For tracking glass views on p13n.js, we were unable to fire the RR.setupGlassViews() function on page load as the onload event is fired even before the function is added. Therefore, we needed to make a server-side change to call this function directly without adding it to the window.onload.


The issue is resolved now.





Push metrics based on siteid level in RRserver and spapi

Created a custom tag for siteid in rrServer and sp-api had a. timer() and made sure metrics are pushed to datadog, in datadog dashboard consumes this metrics to visualise latency at siteId level.




Data Engineering:

Enhanced ThoughtSpot Visualizations for Scorecard Reports

We have upgraded ThoughtSpot with new worksheets and liveboards for essential scorecard reports: Catalog Exposure, Missing Category, Product Exposure, Product Status, and User Session Reports. This enhancement enriches analytics with detailed visualizations for each area.


Bug and Support Fixes

The following issues have been fixed in the release version 24.06 during Mar 07, 2024 - Mar 21, 2024.




General Availability




Show hidden strategies in Product Catalog - Pt 2

There were some strategies that were not visible on the Product Catalog page.


The optimization managers wanted to see all the enabled strategies so that they could preview the recommendations.


Now all the ‘RelatedToCart’ Omni and Offline strategies are visible in the Product Catalog.





Removed properties file in resources of rrMapReduce

Recently we discovered an issue of rrMapreduce jar being reverted from version 7.18.1 at the place of data rollups due to an error. It was found that rrMapreduce jar in rollup jobs were trying to connect to qa-db despite properties sent in command line.


To resolve the problem, the extra file are removed as it is not a good practice to keep them in jars.




Enterprise Dashboard:

Dynamic Experience report - error when selection Orders metric

The issue in the Dynamic Experiences report, where selecting the Orders metric resulted in an error stating "Data column(s) for axis # Dashboard:

Dynamic Experience report - error when selection Orders metric 0 cannot be of type string," has been resolved. Users can now view Orders metric data in the dynamic experience report without encountering any errors, ensuring a seamless analysis process




Enterprise Dashboard:

Bug fixes to Attribute Top Sellers model in Configurable Strategies UI

The Configurable Strategies UI has been refined to address bugs related to the Attribute Top Sellers model, particularly enhancing attribute seed management and interface usability. Users can now easily modify fixed attribute seeds and seamlessly navigate both attribute name and value fields without encountering previous limitations.





Nullpointerexception on API calls to /personalize

Segments defined using the Browser/OS condition caused the Personalize API to return a NullPointerException. This occurred due to missing user agent details in the API call, which are essential for determining the Browser/OS. The issue has been fixed, and the Personalize API now operates error-free, even in the absence of user agent details.




Enterprise Dashboard:

Campaign UI Displaying Previous Campaign's Contents

The View Multiple Campaigns feature, which previously displayed the contents of the previously viewed campaign in the selected contents section, has been resolved. It now displays the latest content.




Advanced Merchandising, Enterprise Dashboard: Unable to select attributes in Advanced Merchandising rule in Firefox Browser

We resolved a Firefox-specific UI bug that prevented users from selecting attributes in Advanced Merchandising rules. The issue, causing selection windows to close abruptly, hindered rule configuration.

The fix ensures stable functionality across key browsers, enabling smooth attribute selection in Firefox and Chrome without needing to switch browsers.





Edge Cases in EngageSAD Job Geo Information Processing

We have addressed specific edge cases in the engageSAD job that were causing exceptions during execution in production. With these corrections, the engageSAD job now runs successfully without errors, enhancing reliability and performance.


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